1,720,973 research outputs found
Comparative Analysis of Ensemble-Based Nowcasting Models for Precipitation Prediction in the UAE
No full textInternational audienceAccurate and timely nowcasting is vital in safeguarding public property and ensuring people's safety, particularly in regions with infrequent but potentially hazardous rainfall events like the United Arab Emirates (UAE). The UAE experiences rare rainfall occurrences that can cause flooding, making it crucial to address these weather challenges. In this study, we compared the predictability of two widely used ensemble-based nowcasting systems, STEPS and LINDA, to assess their performance during various precipitation events in the UAE. Our ROC analysis showed that both systems have a commendable detection rate of over 0.70, a low false alarm rate of less than 0.2, and a substantial area under the curve exceeding 0.75%. Additionally, both algorithms demonstrated their ability to produce reliable nowcasts for up to two hours, using a 5 mm/h threshold. These findings highlight the potential of these models in mitigating the impacts of rare rainfall events, such as flooding, and emphasize the importance of investing in advanced nowcasting technologies for improved weather prediction and public safety
On Solving the Physicians Scheduling Problem at an Emergency Department: A Case Study from Canada
No full textInternational audienceEmergency departments play a pivotal role in healthcare delivery since they represent the first line in hospitals to face emergency patients. This department is where medical care is provided to patients whose arrival rate is uncertain and a high fluctuation in the daily requirements is presented. Yet, managing its human resources efficiently is imperative to improve the quality of its services and provide the right treatments at the right time. This work aims to establish a schedule for physicians in emergency departments which represents a challenging task as it requires respecting several rules incorporating diverse aspects. The objective of this study is to allocate workdays and shifts to emergency department physicians to align physicians’ productivity with patients’ demands, without decreasing physicians’ preferences, to maximize the number of patients treated at the emergency department. Taking into consideration the stochastic and time-varying patients’ demand, we presented a mathematical programming approach that determines feasible physicians’ schedules respecting the emergency department’s hard requirements, minimizing patients’ coverage violations, along with the violation of physicians’ preferences. A real-life case study was carried out in a public Hospital in Quebec, Canada. Different scenarios were then created to study the effect of each goal on the problem. Afterward, we applied a meta-heuristic solution approach belonging to the iterative evolutionary algorithms with several feature configurations to solve the physicians scheduling problem. The results obtained from the computational study of the several scenarios were discussed as a function of the degree of satisfaction of the goals under which the system operates allowing to conclude the best scenario that generates a one-month schedule respecting all goals without deterioration
A Computational Study for the Steiner Tree Problem with Revenue, Budget and Hop Constraints
No full textWe address the Steiner tree problem with revenues, budget and hop constraints (STPRBH), which is a generalization of the well-known Steiner tree problem. Given a connected undirected graph, a root node, edge costs and delays, nodes revenues, as well as a preset budget and hop, the STPRBH seeks to find a subtree that includes the root node, satisfies bound constraints on the total edge cost as well as the number of edges between any node and the root node, while maximizing the sum of the total node revenues. We focus on investigating polynomial-sized formulations. First, we propose an enhanced formulation based on the Miller-Tucker-Zemlin subtour constraints. Next, we investigate a nonlinear MIP formulation that is linearized using the Reformulation-Linearization Technique (RLT). We present the results of a comprehensive computational study of the proposed formulations. These result provide evidence that benchmark instances with up to 500 nodes can be effectively solved using the proposed RLT-based formulation
Approche Heuristique pour un Partitionnement Territorial Équilibré, Compact et Contigu.
No full textInternational audienceApproche Heuristique pour un Partitionnement Territorial Équilibré, Compact et Contigu
Editorial: Novel reliable approaches for prediction and clinical decision-making in cancer
No full textInternational audienceAlthough significant progress has been made in recent decades in understanding thedevelopment and progression of cancer, cancer remains one of the leading causes of death.Recent insights on immunobiological dysregulations involved in the development andprogression of cancer demonstrate the complexity and heterogeneity of cancer, which playcrucial role in the pharmacokinetic variability of cancer therapies. With regard to theprevalence of recurrence/metastases and prognosis, as well as the prediction of cancertreatment success, further investigations are urgently needed to establish cancer signaturesor treatment modalities that enable improved risk stratification and improved patientmanagement. This Research Topic focuses on studies that integrate new comprehensivesystemic, combinatorial, or complexed data that could be useful to develop personalizedtreatment regimens, to improve immunotherapies and clinical decision-making
Exploring Cognitive Sustainability Concerns in Public Responses to Extreme Weather Events: An NLP Analysis of Twitter Data
No full textThe United States has a long history of experiencing extreme weather events. Hurricanes are among the most devastating natural disasters that have significant economic and physical impacts on the country. By applying Natural Language Processing (NLP) to Twitter data for sentiment analysis, emotion detection, and topic modelling, this study provides a more thorough understanding of public response and concerns during five study cases of hurricanes that hit the United States: Harvey, Irma, Maria, Ida, and Ian. The findings on sentiment analysis revealed that 64.75% of the tweets were classified as Negative and 35.25% as Positive. For emotion detection, the predominant emotion was anger, with 39.91%. These results were centred around the main public concerns shown by the topic modelling: hurricane management, donation and support, and disaster impacts. Our future work will focus on understanding people’s responses to extreme weather events through the evolving concept of Cognitive Sustainability
Healthcare facilities spatial layout design: a review on case studies
No full textInternational audienceThe spatial layout design of healthcare facilities plays a critical role in ensuring the efficiency of patient flow, staff movement, and material logistics. As hospitals face increasing demand and overcrowding, the need for adaptable and well-planned configurations becomes ever more pressing. This review focuses on studies that have applied various methodologies to real-world case studies within this context. Although significant advancements have been made in flow analysis, wayfinding systems, and spatial layout planning, several critical gaps persist. These include the limited integration of emerging technologies into the design process, the absence of holistic, systematic frameworks for designing optimally functioning hospitals from inception, and insufficient interdisciplinary collaboration among architects, healthcare administrators, and end-users like medical professionals and patients. The findings of this review point toward future research opportunities aimed at closing these gaps, while also emphasizing the importance of further comparative studies to understand discrepancies between theoretical models and practical applications
Satisfaction des Professionnels de Santé et Optimisation des Processus : Une Approche Intégrée
No full textInternational audienceÉtant leurs principaux clients, la satisfaction des médecins prescripteurs constitue un indicateur clé pour évaluer la qualité des services fournis par les laboratoires d’analyses médicales rattachés aux hôpitaux. Dans cette étude, nous nous intéressons à l’évaluation de la performance du laboratoire d’analyse d’un hôpital universitaire tunisien à travers l’évaluation de la satisfaction de ses médecins prescripteurs et l’analyse du processus de prise en charge. Après la collecte des données, une analyse approfondie a été menée en utilisant divers outils statistiques, notamment l’Analyse enComposantes Principales (ACP) afin d’extraire les informations les plus pertinentes des données issues d’une enquête de satisfaction. Sur la base des résultats obtenus et des points faibles identifiés, une cartographie des flux a été réalisée à l’aide de la cartographie de la chaine de valeur (Value Stream Mapping, VSM) pour analyser les processus du laboratoire et proposer des améliorations. Cette approche intégrée permet non seulement d’optimiser le fonctionnement du laboratoire, mais aussi d’améliorer l’expérience des médecins prescripteurs, en réduisant les inefficacités et en augmentant la qualité du service rendu
Human-Centered Scheduling with a Heterogeneous Workforce in the Context of Industry 5.0: A Proof of Concept from a Learning Factory
No full textInternational audienceThis research focuses on task allocation in a diverse workforce to address the challenges modern companies face in the competitive business environment within the context of Industry 5.0. It examines the potential of Simulation-Based Optimization (SBO) in resolving these issues. Initially, a mathematical model is developed to validate the simulation model’s solution to the optimization problem involving task allocation under deterministic conditions. It then extends its analysis to consider the complexities of stochastic situations while accounting for differences in task execution time. This project aims to identify the mathematical model’s limitations in addressing these challenges and demonstrate the potential of Simulation-Based Optimization as a valuable alternative in considering human behavior
New Lagrangian Relaxation Approach for the Discrete Cost Multicommodity Network Design Problem
No full textWe aim to derive effective lower bounds for the Discrete Cost Multicommodity Network Design Problem (DCMNDP). Given an undirected graph, the problem requires installing at most one facility on each edge such that a set of point-to-point commodity flows can be routed and costs are minimized. In the literature, the Lagrangian relaxation is usually applied to an arc-based formulation to derive lower bounds. In this work, we investigate a path-based formulation and we solve its Lagrangian relaxation using several non-differentiable optimization techniques. More precisely, we devised six variants of the deflected subgradient procedures, using various direction-search and step-length strategies. The computational performance of these Lagrangian-based approaches are evaluated and compared on a set of randomly generated instances, and real-world problems
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